AI Language Identification System

A system for identifying programming languages in source code files using machine learning. This implementation supports both a legacy TensorFlow-based model and a new scikit-learn-based model with improved performance and resource efficiency.

Features

• Supports 8 programming languages: Python, Java, C++, Groovy, JavaScript, XML, JSON, and YAML
• Resource-efficient implementation (runs on CPU with <512MB RAM)
• Fast prediction (more than 4 files/second)
• Handles class imbalance through balanced class weights
• Provides confidence scores for predictions
• Supports batch processing of multiple files

Requirements

• Python 3.6+
• Dependencies listed in requirements.txt

Installation

1. Clone the repository
2. Install dependencies:

   pip install -r requirements.txt

Usage

Training the Model

python ai_predict_lang.py --train --train-dir file

Predicting Language (CLI)

Single file:

python ai_predict_lang.py --file path/to/file.txt

Directory (batch) prediction:

python ai_predict_lang.py --dir path/to/directory/

Show top-k predictions:

python ai_predict_lang.py --file path/to/file.txt --top-k 3

Other options:

• --model-dir: Directory containing trained model (default: 'models')
• --output: Output file for batch results
• --validate: Validate predictions against file extensions (for batch)
• --legacy: Use legacy TensorFlow model

Implementation Details

Model Architecture

The new implementation uses:

• HashingVectorizer with character n-grams (1-3) for feature extraction
• SGDClassifier (logistic regression) with balanced class weights
• Lightweight, language-specific features (keywords, syntax markers, comment styles)
• Efficient memory usage and fast inference

Resource Constraints

The implementation is optimized for:

• Single quad-core CPU
• 512MB RAM
• Local file storage
• Processing > 4 files/second

Results

• Test accuracy: ~93%
• Validation accuracy: ~94%
• Macro F1-score: ~0.78 (test set)
• Weighted F1-score: ~0.94 (test set)
• Resource usage: Peak memory ~139MB, training time ~75s, prediction speed >4 files/sec

Limitations

1. May struggle with:

   • Very short files
   • Files with mixed languages
   • Files with non-standard extensions
   • Binary files or non-UTF-8 encoded files

2. Resource constraints:

   • Limited to CPU processing
   • Memory usage must stay under 512MB
   • Processing speed target of 4 files/second

Future Improvements

1. Model improvements:

   • Add file extension as a feature
   • Implement ensemble methods
   • Add confidence thresholds
   • Create language-specific preprocessing rules

2. Performance optimizations:

   • Implement batch processing for training
   • Add caching for frequently accessed files
   • Optimize feature extraction pipeline

3. Additional features:

   • Support for more languages
   • Better handling of mixed-language files
   • Improved error handling and logging
   • API for integration with other tools